plot_model {ttgsea}

R Documentation

visualization of the model architecture

Description

You are allowed to create a visualization of your model architecture. This architecture displays the information about the name, input shape, and output shape of layers in a flowchart.

Usage

plot_model(x)

Arguments

x	deep learning model

Value

plot for the model architecture

Author(s)

Dongmin Jung

See Also

purrr::map, purrr::map_chr, purrr::pluck, purrr::imap_dfr, DiagrammeR::grViz

Examples

library(reticulate)
if (keras::is_keras_available() & reticulate::py_available()) {
  inputs1 <- layer_input(shape = c(1000))
  inputs2 <- layer_input(shape = c(1000))
  
  predictions1 <- inputs1 %>%
    layer_dense(units = 128, activation = 'relu') %>%
    layer_dense(units = 64, activation = 'relu') %>%
    layer_dense(units = 32, activation = 'softmax')
  
  predictions2 <- inputs2 %>%
    layer_dense(units = 128, activation = 'relu') %>%
    layer_dense(units = 64, activation = 'relu') %>%
    layer_dense(units = 32, activation = 'softmax')
  
  combined <- layer_concatenate(c(predictions1, predictions2)) %>%
    layer_dense(units = 16, activation = 'softmax')
  
  
  model <- keras_model(inputs = c(inputs1, inputs2),
                       outputs = combined)
  plot_model(model)
}

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